Conventional systems relating to data warehousing and other similar domains can process tens or even hundreds of terabytes of data per day and can generate tables with different schemas and sizes for users to run queries to extract information and perform other analysis on those data. In some cases individual data tables can reach or even exceed one terabyte in size for a given day. Hence, analysis over a one-year period can mean hundreds to even thousands of terabytes of data need to be processed by an associated query engine.
Using traditional relational database system for such large data warehouses is generally not feasible due to issues with scalability, performance, and cost. As a result, conventional frontend reporting platforms do not provide adequate flexibility or features with respect to querying data at the backend, particularly querying data residing in nested fields of the data table.